Censoring occurs when complete follow-up time information is unavailable for patients 9 enrolled in a clinical study. The process is considered to be informative (nonignorable) if the 10 likelihood function for the censoring model cannot be partitioned into a set of response parameters 11 that are independent of the censoring parameters. In such cases, estimated survival time 12 probabilities m...

Objectives Methods accounting for competing risks in time-to-event problems are becoming common in mainstream statistical analyses. Standard approaches include those based on log-rank type tests [1] and cumulative incidence regression [2]. These approaches are based on weighting competing events by the censoring distribution. The usual cumulative incidence regression uses weights based on the p...

Extending the model of progressive Type-II censoring, an adaption process is introduced. It allows to choose the next censoring number taking into account both the previous censoring numbers and the previous failure times. After deriving some distributional results, it is shown that maximum likelihood estimators coincide with those in deterministic progressive Type-II censoring. Finally, infere...

A basic and inevitable problem in estimating flow duration distribution arises from “censoring” (i.e., cutting off) the observed flow duration because of a finite measurement period. We extended the KaplanMeier method, which is used in the survival analysis field, and applied it to recover information on the flow duration distribution that was lost due to censoring. We show that the flow durati...

The Cox proportional hazards model is often used to analyse time-to-event data. In many patients, the event time is unknown, either due to dropout or study end. This is known as censoring. When using the Cox proportional hazards model, it is assumed that censoring is independent of event time. However, this is an untestable, often implausible assumption; it may be that censoring is associated w...

Dependent censoring arises in biomedical studies when the survival outcome of interest is censored by competing risks. In survival data with microarray gene expressions, gene selection based on the univariate Cox regression analyses has been used extensively in medical research, which however, is only valid under the independent censoring assumption. In this paper, we first consider a copula-ba...

For random right censoring the completeness of the censoring indicators, ordered according to observation time, is shown.

Independent censoring is a crucial assumption in survival analysis. However, this is impractical in many medical studies, where the presence of dependent censoring leads to difficulty in analyzing covariate effects on disease outcomes. The semicompeting risks framework offers one approach to handling dependent censoring. There are two representative estimators based on an artificial censoring t...

In time-to-event analyses, artificial censoring with correction for induced selection bias using inverse probability-of-censoring weights can be used to 1) examine the natural history of a disease after effective interventions are widely available, 2) correct bias due to noncompliance with fixed or dynamic treatment regimens, and 3) estimate survival in the presence of competing risks. Artifici...

We present an R package, msSurv, to calculate the marginal (that is, not conditional on any covariates) state occupation probabilities, the state entry and exit time distributions, and the marginal integrated transition hazard for a general, possibly non-Markov, multistate system under left-truncation and right censoring. For a Markov model, msSurv also calculates and returns the transition pro...